<?php

    /**
     * PHPExcel_Best_Fit
     * Copyright (c) 2006 - 2015 PHPExcel
     * This library is free software; you can redistribute it and/or
     * modify it under the terms of the GNU Lesser General Public
     * License as published by the Free Software Foundation; either
     * version 2.1 of the License, or (at your option) any later version.
     * This library is distributed in the hope that it will be useful,
     * but WITHOUT ANY WARRANTY; without even the implied warranty of
     * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
     * Lesser General Public License for more details.
     * You should have received a copy of the GNU Lesser General Public
     * License along with this library; if not, write to the Free Software
     * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
     * @category   PHPExcel
     * @package    PHPExcel_Shared_Trend
     * @copyright  Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
     * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt    LGPL
     * @version    ##VERSION##, ##DATE##
     */
    class PHPExcel_Best_Fit {
        /**
         * Indicator flag for a calculation error
         * @var    boolean
         **/
        protected $error = false;
        /**
         * Algorithm type to use for best-fit
         * @var    string
         **/
        protected $bestFitType = 'undetermined';
        /**
         * Number of entries in the sets of x- and y-value arrays
         * @var    int
         **/
        protected $valueCount = 0;
        /**
         * X-value dataseries of values
         * @var    float[]
         **/
        protected $xValues = [];
        /**
         * Y-value dataseries of values
         * @var    float[]
         **/
        protected $yValues = [];
        /**
         * Flag indicating whether values should be adjusted to Y=0
         * @var    boolean
         **/
        protected $adjustToZero = false;
        /**
         * Y-value series of best-fit values
         * @var    float[]
         **/
        protected $yBestFitValues = [];
        protected $goodnessOfFit = 1;
        protected $stdevOfResiduals = 0;
        protected $covariance = 0;
        protected $correlation = 0;
        protected $SSRegression = 0;
        protected $SSResiduals = 0;
        protected $DFResiduals = 0;
        protected $f = 0;
        protected $slope = 0;
        protected $slopeSE = 0;
        protected $intersect = 0;
        protected $intersectSE = 0;
        protected $xOffset = 0;
        protected $yOffset = 0;

        /**
         * Define the regression
         * @param    float[] $yValues The set of Y-values for this regression
         * @param    float[] $xValues The set of X-values for this regression
         * @param    boolean $const
         */
        public function __construct($yValues, $xValues = [], $const = true) {
            //    Calculate number of points
            $nY = count($yValues);
            $nX = count($xValues);
            //    Define X Values if necessary
            if ($nX == 0) {
                $xValues = range(1, $nY);
                $nX      = $nY;
            } elseif ($nY != $nX) {
                //    Ensure both arrays of points are the same size
                $this->error = true;
                return false;
            }
            $this->valueCount = $nY;
            $this->xValues    = $xValues;
            $this->yValues    = $yValues;
        }

        public function getError() {
            return $this->error;
        }

        public function getBestFitType() {
            return $this->bestFitType;
        }

        /**
         * Return the X-Value for a specified value of Y
         * @param     float $yValue Y-Value
         * @return     float                        X-Value
         */
        public function getValueOfXForY($yValue) {
            return false;
        }

        /**
         * Return the original set of X-Values
         * @return     float[]                X-Values
         */
        public function getXValues() {
            return $this->xValues;
        }

        /**
         * Return the Equation of the best-fit line
         * @param     int $dp Number of places of decimal precision to display
         * @return     string
         */
        public function getEquation($dp = 0) {
            return false;
        }

        /**
         * Return the Slope of the line
         * @param     int $dp Number of places of decimal precision to display
         * @return     string
         */
        public function getSlope($dp = 0) {
            if ($dp != 0) {
                return round($this->slope, $dp);
            }
            return $this->slope;
        }

        /**
         * Return the standard error of the Slope
         * @param     int $dp Number of places of decimal precision to display
         * @return     string
         */
        public function getSlopeSE($dp = 0) {
            if ($dp != 0) {
                return round($this->slopeSE, $dp);
            }
            return $this->slopeSE;
        }

        /**
         * Return the Value of X where it intersects Y = 0
         * @param     int $dp Number of places of decimal precision to display
         * @return     string
         */
        public function getIntersect($dp = 0) {
            if ($dp != 0) {
                return round($this->intersect, $dp);
            }
            return $this->intersect;
        }

        /**
         * Return the standard error of the Intersect
         * @param     int $dp Number of places of decimal precision to display
         * @return     string
         */
        public function getIntersectSE($dp = 0) {
            if ($dp != 0) {
                return round($this->intersectSE, $dp);
            }
            return $this->intersectSE;
        }

        /**
         * Return the goodness of fit for this regression
         * @param     int $dp Number of places of decimal precision to return
         * @return     float
         */
        public function getGoodnessOfFit($dp = 0) {
            if ($dp != 0) {
                return round($this->goodnessOfFit, $dp);
            }
            return $this->goodnessOfFit;
        }

        public function getGoodnessOfFitPercent($dp = 0) {
            if ($dp != 0) {
                return round($this->goodnessOfFit * 100, $dp);
            }
            return $this->goodnessOfFit * 100;
        }

        /**
         * Return the standard deviation of the residuals for this regression
         * @param     int $dp Number of places of decimal precision to return
         * @return     float
         */
        public function getStdevOfResiduals($dp = 0) {
            if ($dp != 0) {
                return round($this->stdevOfResiduals, $dp);
            }
            return $this->stdevOfResiduals;
        }

        public function getSSRegression($dp = 0) {
            if ($dp != 0) {
                return round($this->SSRegression, $dp);
            }
            return $this->SSRegression;
        }

        public function getSSResiduals($dp = 0) {
            if ($dp != 0) {
                return round($this->SSResiduals, $dp);
            }
            return $this->SSResiduals;
        }

        public function getDFResiduals($dp = 0) {
            if ($dp != 0) {
                return round($this->DFResiduals, $dp);
            }
            return $this->DFResiduals;
        }

        public function getF($dp = 0) {
            if ($dp != 0) {
                return round($this->f, $dp);
            }
            return $this->f;
        }

        public function getCovariance($dp = 0) {
            if ($dp != 0) {
                return round($this->covariance, $dp);
            }
            return $this->covariance;
        }

        public function getCorrelation($dp = 0) {
            if ($dp != 0) {
                return round($this->correlation, $dp);
            }
            return $this->correlation;
        }

        public function getYBestFitValues() {
            return $this->yBestFitValues;
        }

        protected function leastSquareFit($yValues, $xValues, $const) {
            // calculate sums
            $x_sum = array_sum($xValues);
            $y_sum = array_sum($yValues);
            $meanX = $x_sum / $this->valueCount;
            $meanY = $y_sum / $this->valueCount;
            $mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
            for ($i = 0; $i < $this->valueCount; ++$i) {
                $xy_sum += $xValues[$i] * $yValues[$i];
                $xx_sum += $xValues[$i] * $xValues[$i];
                $yy_sum += $yValues[$i] * $yValues[$i];
                if ($const) {
                    $mBase    += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
                    $mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
                } else {
                    $mBase    += $xValues[$i] * $yValues[$i];
                    $mDivisor += $xValues[$i] * $xValues[$i];
                }
            }
            // calculate slope
            //        $this->slope = (($this->valueCount * $xy_sum) - ($x_sum * $y_sum)) / (($this->valueCount * $xx_sum) - ($x_sum * $x_sum));
            $this->slope = $mBase / $mDivisor;
            // calculate intersect
            //        $this->intersect = ($y_sum - ($this->slope * $x_sum)) / $this->valueCount;
            if ($const) {
                $this->intersect = $meanY - ($this->slope * $meanX);
            } else {
                $this->intersect = 0;
            }
            $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $const);
        }

        protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const) {
            $SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;
            foreach ($this->xValues as $xKey => $xValue) {
                $bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
                $SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
                if ($const) {
                    $SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY);
                } else {
                    $SStot += $this->yValues[$xKey] * $this->yValues[$xKey];
                }
                $SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);
                if ($const) {
                    $SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
                } else {
                    $SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
                }
            }
            $this->SSResiduals = $SSres;
            $this->DFResiduals = $this->valueCount - 1 - $const;
            if ($this->DFResiduals == 0.0) {
                $this->stdevOfResiduals = 0.0;
            } else {
                $this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
            }
            if (($SStot == 0.0) || ($SSres == $SStot)) {
                $this->goodnessOfFit = 1;
            } else {
                $this->goodnessOfFit = 1 - ($SSres / $SStot);
            }
            $this->SSRegression = $this->goodnessOfFit * $SStot;
            $this->covariance   = $SScov / $this->valueCount;
            $this->correlation  = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - pow($sumX, 2)) * ($this->valueCount * $sumY2 - pow($sumY, 2)));
            $this->slopeSE      = $this->stdevOfResiduals / sqrt($SSsex);
            $this->intersectSE  = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2));
            if ($this->SSResiduals != 0.0) {
                if ($this->DFResiduals == 0.0) {
                    $this->f = 0.0;
                } else {
                    $this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals);
                }
            } else {
                if ($this->DFResiduals == 0.0) {
                    $this->f = 0.0;
                } else {
                    $this->f = $this->SSRegression / $this->DFResiduals;
                }
            }
        }

        /**
         * Return the Y-Value for a specified value of X
         * @param     float $xValue X-Value
         * @return     float                        Y-Value
         */
        public function getValueOfYForX($xValue) {
            return false;
        }
    }
